Browsing by Author "Varol C."

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    A cellular automata-based approach for spatio-temporal modeling of the city center as a complex system: The case of Kastamonu, Türkiye
    (2023-01-01) Isinkaralar O.; Varol C.
    Cities are located at the intersection of global interactions and analytical modeling of space is an essential progression to understand the organizational structure of today's cities, which consist of complex networks and self-organizing processes that affect their nonlinear development. The cellular automata-Markov chain (CA-MC) modeling is a preferred method in predictive modeling and land use change studies of complex systems. It is widely used in modeling land use/land cover change. In this paper, the land use change between 1985-2021 in the Kastamonu city center has been examined within the framework of complexity theory. It is aimed to develop a quantitative model for the comparative measurement of temporal complexity variation. In this context, scenarios were designed with two basic approaches; self-organizing and planned city center development and simulations were made for the years 2031 and 2057. The agreement of the model was tested with Kappa statistical values, which resulted to be >0.9438.
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    Digital mapping and predicting the urban growth: integrating scenarios into cellular automata—Markov chain modeling
    (2022-12-01) Isinkaralar O.; Varol C.; Yilmaz D.
    Predictive modeling and land use/land cover change studies in complex systems are well advanced. Cellular automata (CA)-Markov chain (MC) can be defined as one frequently preferred method for this purpose. This paper aims to adapt the CA-MC model to the simulation of residential areas in the city. The proposed method was tested in the city center of Kastamonu, Türkiye, using four time periods: 1985, 2011, 2015, and 2021. Spatio-temporal change maps were produced using ArcGIS 10.0 software. Land use simulation of the urban center, including residence units for 2031 and 2057, was performed using the integrated CA-MC technique. The method’s suitability was demonstrated with the Kappa index of agreement values (Kstandart: 0.93; Klocation: 0.98; Kno: 0.98; and KlocationStrata: 0.95). Within the scope of the study, two different scenarios were designed as short term (S1) and long term (S2). According to the predictions for 2031, there was a residential area increase of 15% in S1 and 29% in S2. When we reach 2057, these growth values were measured as 50% according to S1 and 72% according to S2.